Approximately every two years, the resolution of medical imaging devices doubles, leading to ever larger numbers of pixels to be inspected and evaluated by clinicians, if it is assumed that increasing resolution increases quality of care and clinical outcome. Together with the fact that there are limits to what society is willing to spend on health care, and a decreasing efficiency of physicians and other care providers, this means that computer analysis of these images has to rapidly become dependable enough to increase the quality, cost-effectiveness, and efficiency of diagnostic screening, and disease progression evaluation.
The essence of computer assisted diagnosis of images is to assign a score or number to an image, in an objective, reproducible manner, so that the disease information in an image can be dealt with in the same way as a comparatively simple to interpret blood pressure reading, a weight, or serum glucose level.
The three leading causes of blindness in the Western world are age related macular degeneration (approximately 500,000 new cases of blindness annually in the US alone), diabetic retinopathy (25,000 cases of blindness annually), and glaucoma (20,000 cases of blindness annually). All affect primarily structures in the retina: the macula, the optic disc and the entire retina respectively, all require retinal imaging of some form for diagnosis and management. Because effective treatment for diabetic retinopathy, glaucoma and just now with VEGF inhibitors, macular degeneration, is available if the disease is diagnosed timely, screening for these diseases is effective, and has in fact shown to be cost-effective for patients with diabetes. Because the numbers of patients at risk are huge, in the tens of millions, and would have to be examined regularly by trained experts, computer assisted diagnosis of these images is essential.
Even more importantly, with increased availability of genetic testing for the risk of developing retinal diseases, patients will have to be examined to ascertain whether they have the diseases. Analysis of retinal images will allow much more precise targeting of genetic testing, as well as better scoring for the disease to which the patient is at risk.
The limiting step in image analysis algorithms has become that they mimic the limited visual system of expert clinicians. The current approach to computer assisted lesion detection and segmentation of image structures is limited as it is asymptotically approaching the capabilities of the human visual system of the experts that initially annotated the images. The human visual system cannot appreciate all information available in an image or scene. In addition, annotation of images for machine learning algorithms will be a bottleneck.
What are needed are methods and systems that automate the process of image analysis and are not limited by the human visual system.